Full course coming soon

AI Infrastructure Economics: Accelerators, Cost, and Capacity

7 chapters · 31 lessons

1. 1. AI Workload Fundamentals and Compute Patterns

   4 lessons

   Learn how different AI tasks create distinct demands on hardware and why general CPUs struggle with ML workloads.

2. 2. Accelerator Design Principles and Tradeoffs

   4 lessons

   Understand how specialized processors optimize for AI tasks and what design choices mean for real applications.

3. 3. Infrastructure Cost Modeling for AI Products

   5 lessons

   Learn to estimate and compare costs across training, serving, and experimentation workloads.

4. 4. Custom Silicon vs Cloud Accelerators

   5 lessons

   Evaluate when custom chips make sense and when off-the-shelf solutions are more practical.

5. 5. Workload Profiling and Capacity Planning

   4 lessons

   Learn practical methods to measure actual resource usage and forecast infrastructure needs.

6. 6. System-Level Performance and Optimization

   4 lessons

   Understand how software, networking, and data center design affect accelerator effectiveness.

7. 7. Making Infrastructure Decisions in Practice

   5 lessons

   Apply frameworks to real scenarios and learn to communicate infrastructure constraints to stakeholders.